{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore') \n",
    "%matplotlib inline\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- State:州名\n",
    "- Account Length:账户长度\n",
    "- Area Code：区号\n",
    "- Phone：电话号码\n",
    "- ‘Int'l Plan：国际策略活动\n",
    "- VMail Plan：参与活动\n",
    "- VMail Message：语音邮箱\n",
    "- Day Mins:白天通话分钟数\n",
    "- Day Calls:白天打电话个数\n",
    "- Day Charge:白天收费情况\n",
    "- Eve Mins:晚间通话分钟数\n",
    "- Eve Calls：晚间打电话个数\n",
    "- Eve Charge：晚间收费情况\n",
    "- Night Mins：夜间通话分钟数\n",
    "- Night Calls：夜间打电话个数\n",
    "- Night Charge：夜间收费情况 \n",
    "- Intl Mins：国际通话分钟数\n",
    "- Intl Calls：国际打电话个数\n",
    "- Intl Charge：国际收费\n",
    "- CustServ Calls：客服电话数量\n",
    "- Churn：流失与否"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['State',\n",
       " 'Account Length',\n",
       " 'Area Code',\n",
       " 'Phone',\n",
       " \"Int'l Plan\",\n",
       " 'VMail Plan',\n",
       " 'VMail Message',\n",
       " 'Day Mins',\n",
       " 'Day Calls',\n",
       " 'Day Charge',\n",
       " 'Eve Mins',\n",
       " 'Eve Calls',\n",
       " 'Eve Charge',\n",
       " 'Night Mins',\n",
       " 'Night Calls',\n",
       " 'Night Charge',\n",
       " 'Intl Mins',\n",
       " 'Intl Calls',\n",
       " 'Intl Charge',\n",
       " 'CustServ Calls',\n",
       " 'Churn?']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df = pd.read_csv('churn.csv')\n",
    "col_names = churn_df.columns.tolist() #所有的列展示出来\n",
    "col_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3333 entries, 0 to 3332\n",
      "Data columns (total 21 columns):\n",
      "State             3333 non-null object\n",
      "Account Length    3333 non-null int64\n",
      "Area Code         3333 non-null int64\n",
      "Phone             3333 non-null object\n",
      "Int'l Plan        3333 non-null object\n",
      "VMail Plan        3333 non-null object\n",
      "VMail Message     3333 non-null int64\n",
      "Day Mins          3333 non-null float64\n",
      "Day Calls         3333 non-null int64\n",
      "Day Charge        3333 non-null float64\n",
      "Eve Mins          3333 non-null float64\n",
      "Eve Calls         3333 non-null int64\n",
      "Eve Charge        3333 non-null float64\n",
      "Night Mins        3333 non-null float64\n",
      "Night Calls       3333 non-null int64\n",
      "Night Charge      3333 non-null float64\n",
      "Intl Mins         3333 non-null float64\n",
      "Intl Calls        3333 non-null int64\n",
      "Intl Charge       3333 non-null float64\n",
      "CustServ Calls    3333 non-null int64\n",
      "Churn?            3333 non-null object\n",
      "dtypes: float64(8), int64(8), object(5)\n",
      "memory usage: 546.9+ KB\n"
     ]
    }
   ],
   "source": [
    "churn_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Account Length</th>\n",
       "      <th>Area Code</th>\n",
       "      <th>VMail Message</th>\n",
       "      <th>Day Mins</th>\n",
       "      <th>Day Calls</th>\n",
       "      <th>Day Charge</th>\n",
       "      <th>Eve Mins</th>\n",
       "      <th>Eve Calls</th>\n",
       "      <th>Eve Charge</th>\n",
       "      <th>Night Mins</th>\n",
       "      <th>Night Calls</th>\n",
       "      <th>Night Charge</th>\n",
       "      <th>Intl Mins</th>\n",
       "      <th>Intl Calls</th>\n",
       "      <th>Intl Charge</th>\n",
       "      <th>CustServ Calls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>101.064806</td>\n",
       "      <td>437.182418</td>\n",
       "      <td>8.099010</td>\n",
       "      <td>179.775098</td>\n",
       "      <td>100.435644</td>\n",
       "      <td>30.562307</td>\n",
       "      <td>200.980348</td>\n",
       "      <td>100.114311</td>\n",
       "      <td>17.083540</td>\n",
       "      <td>200.872037</td>\n",
       "      <td>100.107711</td>\n",
       "      <td>9.039325</td>\n",
       "      <td>10.237294</td>\n",
       "      <td>4.479448</td>\n",
       "      <td>2.764581</td>\n",
       "      <td>1.562856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>39.822106</td>\n",
       "      <td>42.371290</td>\n",
       "      <td>13.688365</td>\n",
       "      <td>54.467389</td>\n",
       "      <td>20.069084</td>\n",
       "      <td>9.259435</td>\n",
       "      <td>50.713844</td>\n",
       "      <td>19.922625</td>\n",
       "      <td>4.310668</td>\n",
       "      <td>50.573847</td>\n",
       "      <td>19.568609</td>\n",
       "      <td>2.275873</td>\n",
       "      <td>2.791840</td>\n",
       "      <td>2.461214</td>\n",
       "      <td>0.753773</td>\n",
       "      <td>1.315491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>74.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>143.700000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>24.430000</td>\n",
       "      <td>166.600000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>14.160000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>7.520000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>101.000000</td>\n",
       "      <td>415.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>179.400000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>201.400000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>17.120000</td>\n",
       "      <td>201.200000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>9.050000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.780000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>127.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>216.400000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>36.790000</td>\n",
       "      <td>235.300000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>235.300000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>10.590000</td>\n",
       "      <td>12.100000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.270000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>243.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>350.800000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>59.640000</td>\n",
       "      <td>363.700000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>30.910000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>17.770000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Account Length    Area Code  VMail Message     Day Mins    Day Calls  \\\n",
       "count     3333.000000  3333.000000    3333.000000  3333.000000  3333.000000   \n",
       "mean       101.064806   437.182418       8.099010   179.775098   100.435644   \n",
       "std         39.822106    42.371290      13.688365    54.467389    20.069084   \n",
       "min          1.000000   408.000000       0.000000     0.000000     0.000000   \n",
       "25%         74.000000   408.000000       0.000000   143.700000    87.000000   \n",
       "50%        101.000000   415.000000       0.000000   179.400000   101.000000   \n",
       "75%        127.000000   510.000000      20.000000   216.400000   114.000000   \n",
       "max        243.000000   510.000000      51.000000   350.800000   165.000000   \n",
       "\n",
       "        Day Charge     Eve Mins    Eve Calls   Eve Charge   Night Mins  \\\n",
       "count  3333.000000  3333.000000  3333.000000  3333.000000  3333.000000   \n",
       "mean     30.562307   200.980348   100.114311    17.083540   200.872037   \n",
       "std       9.259435    50.713844    19.922625     4.310668    50.573847   \n",
       "min       0.000000     0.000000     0.000000     0.000000    23.200000   \n",
       "25%      24.430000   166.600000    87.000000    14.160000   167.000000   \n",
       "50%      30.500000   201.400000   100.000000    17.120000   201.200000   \n",
       "75%      36.790000   235.300000   114.000000    20.000000   235.300000   \n",
       "max      59.640000   363.700000   170.000000    30.910000   395.000000   \n",
       "\n",
       "       Night Calls  Night Charge    Intl Mins   Intl Calls  Intl Charge  \\\n",
       "count  3333.000000   3333.000000  3333.000000  3333.000000  3333.000000   \n",
       "mean    100.107711      9.039325    10.237294     4.479448     2.764581   \n",
       "std      19.568609      2.275873     2.791840     2.461214     0.753773   \n",
       "min      33.000000      1.040000     0.000000     0.000000     0.000000   \n",
       "25%      87.000000      7.520000     8.500000     3.000000     2.300000   \n",
       "50%     100.000000      9.050000    10.300000     4.000000     2.780000   \n",
       "75%     113.000000     10.590000    12.100000     6.000000     3.270000   \n",
       "max     175.000000     17.770000    20.000000    20.000000     5.400000   \n",
       "\n",
       "       CustServ Calls  \n",
       "count     3333.000000  \n",
       "mean         1.562856  \n",
       "std          1.315491  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          2.000000  \n",
       "max          9.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_df.rename(columns={'Churn?':'Churn','Int\\'l Plan':'Intl Plan'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False.    2850\n",
       "True.      483\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "churn_df['Churn'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_df['Churn'] = np.where(churn_df['Churn']=='True.',1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Account Length</th>\n",
       "      <th>Area Code</th>\n",
       "      <th>Phone</th>\n",
       "      <th>Intl Plan</th>\n",
       "      <th>VMail Plan</th>\n",
       "      <th>VMail Message</th>\n",
       "      <th>Day Mins</th>\n",
       "      <th>Day Calls</th>\n",
       "      <th>Day Charge</th>\n",
       "      <th>...</th>\n",
       "      <th>Eve Calls</th>\n",
       "      <th>Eve Charge</th>\n",
       "      <th>Night Mins</th>\n",
       "      <th>Night Calls</th>\n",
       "      <th>Night Charge</th>\n",
       "      <th>Intl Mins</th>\n",
       "      <th>Intl Calls</th>\n",
       "      <th>Intl Charge</th>\n",
       "      <th>CustServ Calls</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
       "      <td>128</td>\n",
       "      <td>415</td>\n",
       "      <td>382-4657</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
       "      <td>415</td>\n",
       "      <td>371-7191</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>...</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>358-1921</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>...</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>375-9999</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>330-6626</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AL</td>\n",
       "      <td>118</td>\n",
       "      <td>510</td>\n",
       "      <td>391-8027</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>223.4</td>\n",
       "      <td>98</td>\n",
       "      <td>37.98</td>\n",
       "      <td>...</td>\n",
       "      <td>101</td>\n",
       "      <td>18.75</td>\n",
       "      <td>203.9</td>\n",
       "      <td>118</td>\n",
       "      <td>9.18</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MA</td>\n",
       "      <td>121</td>\n",
       "      <td>510</td>\n",
       "      <td>355-9993</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>24</td>\n",
       "      <td>218.2</td>\n",
       "      <td>88</td>\n",
       "      <td>37.09</td>\n",
       "      <td>...</td>\n",
       "      <td>108</td>\n",
       "      <td>29.62</td>\n",
       "      <td>212.6</td>\n",
       "      <td>118</td>\n",
       "      <td>9.57</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7</td>\n",
       "      <td>2.03</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MO</td>\n",
       "      <td>147</td>\n",
       "      <td>415</td>\n",
       "      <td>329-9001</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>79</td>\n",
       "      <td>26.69</td>\n",
       "      <td>...</td>\n",
       "      <td>94</td>\n",
       "      <td>8.76</td>\n",
       "      <td>211.8</td>\n",
       "      <td>96</td>\n",
       "      <td>9.53</td>\n",
       "      <td>7.1</td>\n",
       "      <td>6</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LA</td>\n",
       "      <td>117</td>\n",
       "      <td>408</td>\n",
       "      <td>335-4719</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>184.5</td>\n",
       "      <td>97</td>\n",
       "      <td>31.37</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>29.89</td>\n",
       "      <td>215.8</td>\n",
       "      <td>90</td>\n",
       "      <td>9.71</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>2.35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>WV</td>\n",
       "      <td>141</td>\n",
       "      <td>415</td>\n",
       "      <td>330-8173</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>37</td>\n",
       "      <td>258.6</td>\n",
       "      <td>84</td>\n",
       "      <td>43.96</td>\n",
       "      <td>...</td>\n",
       "      <td>111</td>\n",
       "      <td>18.87</td>\n",
       "      <td>326.4</td>\n",
       "      <td>97</td>\n",
       "      <td>14.69</td>\n",
       "      <td>11.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>IN</td>\n",
       "      <td>65</td>\n",
       "      <td>415</td>\n",
       "      <td>329-6603</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>129.1</td>\n",
       "      <td>137</td>\n",
       "      <td>21.95</td>\n",
       "      <td>...</td>\n",
       "      <td>83</td>\n",
       "      <td>19.42</td>\n",
       "      <td>208.8</td>\n",
       "      <td>111</td>\n",
       "      <td>9.40</td>\n",
       "      <td>12.7</td>\n",
       "      <td>6</td>\n",
       "      <td>3.43</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>RI</td>\n",
       "      <td>74</td>\n",
       "      <td>415</td>\n",
       "      <td>344-9403</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>187.7</td>\n",
       "      <td>127</td>\n",
       "      <td>31.91</td>\n",
       "      <td>...</td>\n",
       "      <td>148</td>\n",
       "      <td>13.89</td>\n",
       "      <td>196.0</td>\n",
       "      <td>94</td>\n",
       "      <td>8.82</td>\n",
       "      <td>9.1</td>\n",
       "      <td>5</td>\n",
       "      <td>2.46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>IA</td>\n",
       "      <td>168</td>\n",
       "      <td>408</td>\n",
       "      <td>363-1107</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>128.8</td>\n",
       "      <td>96</td>\n",
       "      <td>21.90</td>\n",
       "      <td>...</td>\n",
       "      <td>71</td>\n",
       "      <td>8.92</td>\n",
       "      <td>141.1</td>\n",
       "      <td>128</td>\n",
       "      <td>6.35</td>\n",
       "      <td>11.2</td>\n",
       "      <td>2</td>\n",
       "      <td>3.02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MT</td>\n",
       "      <td>95</td>\n",
       "      <td>510</td>\n",
       "      <td>394-8006</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>156.6</td>\n",
       "      <td>88</td>\n",
       "      <td>26.62</td>\n",
       "      <td>...</td>\n",
       "      <td>75</td>\n",
       "      <td>21.05</td>\n",
       "      <td>192.3</td>\n",
       "      <td>115</td>\n",
       "      <td>8.65</td>\n",
       "      <td>12.3</td>\n",
       "      <td>5</td>\n",
       "      <td>3.32</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IA</td>\n",
       "      <td>62</td>\n",
       "      <td>415</td>\n",
       "      <td>366-9238</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>120.7</td>\n",
       "      <td>70</td>\n",
       "      <td>20.52</td>\n",
       "      <td>...</td>\n",
       "      <td>76</td>\n",
       "      <td>26.11</td>\n",
       "      <td>203.0</td>\n",
       "      <td>99</td>\n",
       "      <td>9.14</td>\n",
       "      <td>13.1</td>\n",
       "      <td>6</td>\n",
       "      <td>3.54</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NY</td>\n",
       "      <td>161</td>\n",
       "      <td>415</td>\n",
       "      <td>351-7269</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>332.9</td>\n",
       "      <td>67</td>\n",
       "      <td>56.59</td>\n",
       "      <td>...</td>\n",
       "      <td>97</td>\n",
       "      <td>27.01</td>\n",
       "      <td>160.6</td>\n",
       "      <td>128</td>\n",
       "      <td>7.23</td>\n",
       "      <td>5.4</td>\n",
       "      <td>9</td>\n",
       "      <td>1.46</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ID</td>\n",
       "      <td>85</td>\n",
       "      <td>408</td>\n",
       "      <td>350-8884</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>27</td>\n",
       "      <td>196.4</td>\n",
       "      <td>139</td>\n",
       "      <td>33.39</td>\n",
       "      <td>...</td>\n",
       "      <td>90</td>\n",
       "      <td>23.88</td>\n",
       "      <td>89.3</td>\n",
       "      <td>75</td>\n",
       "      <td>4.02</td>\n",
       "      <td>13.8</td>\n",
       "      <td>4</td>\n",
       "      <td>3.73</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>VT</td>\n",
       "      <td>93</td>\n",
       "      <td>510</td>\n",
       "      <td>386-2923</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>190.7</td>\n",
       "      <td>114</td>\n",
       "      <td>32.42</td>\n",
       "      <td>...</td>\n",
       "      <td>111</td>\n",
       "      <td>18.55</td>\n",
       "      <td>129.6</td>\n",
       "      <td>121</td>\n",
       "      <td>5.83</td>\n",
       "      <td>8.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.19</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>VA</td>\n",
       "      <td>76</td>\n",
       "      <td>510</td>\n",
       "      <td>356-2992</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>33</td>\n",
       "      <td>189.7</td>\n",
       "      <td>66</td>\n",
       "      <td>32.25</td>\n",
       "      <td>...</td>\n",
       "      <td>65</td>\n",
       "      <td>18.09</td>\n",
       "      <td>165.7</td>\n",
       "      <td>108</td>\n",
       "      <td>7.46</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>TX</td>\n",
       "      <td>73</td>\n",
       "      <td>415</td>\n",
       "      <td>373-2782</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>224.4</td>\n",
       "      <td>90</td>\n",
       "      <td>38.15</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>13.56</td>\n",
       "      <td>192.8</td>\n",
       "      <td>74</td>\n",
       "      <td>8.68</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.51</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>FL</td>\n",
       "      <td>147</td>\n",
       "      <td>415</td>\n",
       "      <td>396-5800</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>155.1</td>\n",
       "      <td>117</td>\n",
       "      <td>26.37</td>\n",
       "      <td>...</td>\n",
       "      <td>93</td>\n",
       "      <td>20.37</td>\n",
       "      <td>208.8</td>\n",
       "      <td>133</td>\n",
       "      <td>9.40</td>\n",
       "      <td>10.6</td>\n",
       "      <td>4</td>\n",
       "      <td>2.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CO</td>\n",
       "      <td>77</td>\n",
       "      <td>408</td>\n",
       "      <td>393-7984</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>62.4</td>\n",
       "      <td>89</td>\n",
       "      <td>10.61</td>\n",
       "      <td>...</td>\n",
       "      <td>121</td>\n",
       "      <td>14.44</td>\n",
       "      <td>209.6</td>\n",
       "      <td>64</td>\n",
       "      <td>9.43</td>\n",
       "      <td>5.7</td>\n",
       "      <td>6</td>\n",
       "      <td>1.54</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>AZ</td>\n",
       "      <td>130</td>\n",
       "      <td>415</td>\n",
       "      <td>358-1958</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>112</td>\n",
       "      <td>31.11</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>6.20</td>\n",
       "      <td>181.8</td>\n",
       "      <td>78</td>\n",
       "      <td>8.18</td>\n",
       "      <td>9.5</td>\n",
       "      <td>19</td>\n",
       "      <td>2.57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SC</td>\n",
       "      <td>111</td>\n",
       "      <td>415</td>\n",
       "      <td>350-2565</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>110.4</td>\n",
       "      <td>103</td>\n",
       "      <td>18.77</td>\n",
       "      <td>...</td>\n",
       "      <td>102</td>\n",
       "      <td>11.67</td>\n",
       "      <td>189.6</td>\n",
       "      <td>105</td>\n",
       "      <td>8.53</td>\n",
       "      <td>7.7</td>\n",
       "      <td>6</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>VA</td>\n",
       "      <td>132</td>\n",
       "      <td>510</td>\n",
       "      <td>343-4696</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>81.1</td>\n",
       "      <td>86</td>\n",
       "      <td>13.79</td>\n",
       "      <td>...</td>\n",
       "      <td>72</td>\n",
       "      <td>20.84</td>\n",
       "      <td>237.0</td>\n",
       "      <td>115</td>\n",
       "      <td>10.67</td>\n",
       "      <td>10.3</td>\n",
       "      <td>2</td>\n",
       "      <td>2.78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NE</td>\n",
       "      <td>174</td>\n",
       "      <td>415</td>\n",
       "      <td>331-3698</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>124.3</td>\n",
       "      <td>76</td>\n",
       "      <td>21.13</td>\n",
       "      <td>...</td>\n",
       "      <td>112</td>\n",
       "      <td>23.55</td>\n",
       "      <td>250.7</td>\n",
       "      <td>115</td>\n",
       "      <td>11.28</td>\n",
       "      <td>15.5</td>\n",
       "      <td>5</td>\n",
       "      <td>4.19</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>WY</td>\n",
       "      <td>57</td>\n",
       "      <td>408</td>\n",
       "      <td>357-3817</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>39</td>\n",
       "      <td>213.0</td>\n",
       "      <td>115</td>\n",
       "      <td>36.21</td>\n",
       "      <td>...</td>\n",
       "      <td>112</td>\n",
       "      <td>16.24</td>\n",
       "      <td>182.7</td>\n",
       "      <td>115</td>\n",
       "      <td>8.22</td>\n",
       "      <td>9.5</td>\n",
       "      <td>3</td>\n",
       "      <td>2.57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>MT</td>\n",
       "      <td>54</td>\n",
       "      <td>408</td>\n",
       "      <td>418-6412</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>134.3</td>\n",
       "      <td>73</td>\n",
       "      <td>22.83</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>13.22</td>\n",
       "      <td>102.1</td>\n",
       "      <td>68</td>\n",
       "      <td>4.59</td>\n",
       "      <td>14.7</td>\n",
       "      <td>4</td>\n",
       "      <td>3.97</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>MO</td>\n",
       "      <td>20</td>\n",
       "      <td>415</td>\n",
       "      <td>353-2630</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>109</td>\n",
       "      <td>32.30</td>\n",
       "      <td>...</td>\n",
       "      <td>84</td>\n",
       "      <td>21.95</td>\n",
       "      <td>181.5</td>\n",
       "      <td>102</td>\n",
       "      <td>8.17</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>HI</td>\n",
       "      <td>49</td>\n",
       "      <td>510</td>\n",
       "      <td>410-7789</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>119.3</td>\n",
       "      <td>117</td>\n",
       "      <td>20.28</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>18.28</td>\n",
       "      <td>178.7</td>\n",
       "      <td>90</td>\n",
       "      <td>8.04</td>\n",
       "      <td>11.1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3303</th>\n",
       "      <td>WI</td>\n",
       "      <td>114</td>\n",
       "      <td>415</td>\n",
       "      <td>373-7308</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>26</td>\n",
       "      <td>137.1</td>\n",
       "      <td>88</td>\n",
       "      <td>23.31</td>\n",
       "      <td>...</td>\n",
       "      <td>125</td>\n",
       "      <td>13.23</td>\n",
       "      <td>247.6</td>\n",
       "      <td>94</td>\n",
       "      <td>11.14</td>\n",
       "      <td>11.5</td>\n",
       "      <td>7</td>\n",
       "      <td>3.11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3304</th>\n",
       "      <td>IL</td>\n",
       "      <td>71</td>\n",
       "      <td>510</td>\n",
       "      <td>330-7137</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>186.1</td>\n",
       "      <td>114</td>\n",
       "      <td>31.64</td>\n",
       "      <td>...</td>\n",
       "      <td>140</td>\n",
       "      <td>16.88</td>\n",
       "      <td>206.5</td>\n",
       "      <td>80</td>\n",
       "      <td>9.29</td>\n",
       "      <td>13.8</td>\n",
       "      <td>5</td>\n",
       "      <td>3.73</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3305</th>\n",
       "      <td>IN</td>\n",
       "      <td>58</td>\n",
       "      <td>415</td>\n",
       "      <td>406-8445</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>22</td>\n",
       "      <td>224.1</td>\n",
       "      <td>127</td>\n",
       "      <td>38.10</td>\n",
       "      <td>...</td>\n",
       "      <td>85</td>\n",
       "      <td>20.30</td>\n",
       "      <td>174.2</td>\n",
       "      <td>86</td>\n",
       "      <td>7.84</td>\n",
       "      <td>11.5</td>\n",
       "      <td>7</td>\n",
       "      <td>3.11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3306</th>\n",
       "      <td>AL</td>\n",
       "      <td>106</td>\n",
       "      <td>408</td>\n",
       "      <td>404-5283</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>29</td>\n",
       "      <td>83.6</td>\n",
       "      <td>131</td>\n",
       "      <td>14.21</td>\n",
       "      <td>...</td>\n",
       "      <td>131</td>\n",
       "      <td>17.33</td>\n",
       "      <td>229.5</td>\n",
       "      <td>73</td>\n",
       "      <td>10.33</td>\n",
       "      <td>8.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3307</th>\n",
       "      <td>OK</td>\n",
       "      <td>172</td>\n",
       "      <td>408</td>\n",
       "      <td>398-3632</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>203.9</td>\n",
       "      <td>109</td>\n",
       "      <td>34.66</td>\n",
       "      <td>...</td>\n",
       "      <td>123</td>\n",
       "      <td>19.89</td>\n",
       "      <td>160.7</td>\n",
       "      <td>65</td>\n",
       "      <td>7.23</td>\n",
       "      <td>17.8</td>\n",
       "      <td>4</td>\n",
       "      <td>4.81</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3308</th>\n",
       "      <td>IA</td>\n",
       "      <td>45</td>\n",
       "      <td>415</td>\n",
       "      <td>399-5763</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>211.3</td>\n",
       "      <td>87</td>\n",
       "      <td>35.92</td>\n",
       "      <td>...</td>\n",
       "      <td>97</td>\n",
       "      <td>14.08</td>\n",
       "      <td>265.9</td>\n",
       "      <td>72</td>\n",
       "      <td>11.97</td>\n",
       "      <td>13.3</td>\n",
       "      <td>6</td>\n",
       "      <td>3.59</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3309</th>\n",
       "      <td>VT</td>\n",
       "      <td>100</td>\n",
       "      <td>408</td>\n",
       "      <td>340-9449</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>219.4</td>\n",
       "      <td>112</td>\n",
       "      <td>37.30</td>\n",
       "      <td>...</td>\n",
       "      <td>102</td>\n",
       "      <td>19.18</td>\n",
       "      <td>255.3</td>\n",
       "      <td>95</td>\n",
       "      <td>11.49</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.24</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3310</th>\n",
       "      <td>NY</td>\n",
       "      <td>94</td>\n",
       "      <td>415</td>\n",
       "      <td>363-1123</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>190.4</td>\n",
       "      <td>91</td>\n",
       "      <td>32.37</td>\n",
       "      <td>...</td>\n",
       "      <td>107</td>\n",
       "      <td>7.82</td>\n",
       "      <td>224.8</td>\n",
       "      <td>108</td>\n",
       "      <td>10.12</td>\n",
       "      <td>13.6</td>\n",
       "      <td>17</td>\n",
       "      <td>3.67</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3311</th>\n",
       "      <td>LA</td>\n",
       "      <td>128</td>\n",
       "      <td>415</td>\n",
       "      <td>361-2170</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>147.7</td>\n",
       "      <td>94</td>\n",
       "      <td>25.11</td>\n",
       "      <td>...</td>\n",
       "      <td>83</td>\n",
       "      <td>24.08</td>\n",
       "      <td>188.3</td>\n",
       "      <td>124</td>\n",
       "      <td>8.47</td>\n",
       "      <td>6.9</td>\n",
       "      <td>5</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3312</th>\n",
       "      <td>SC</td>\n",
       "      <td>181</td>\n",
       "      <td>408</td>\n",
       "      <td>406-6304</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>229.9</td>\n",
       "      <td>130</td>\n",
       "      <td>39.08</td>\n",
       "      <td>...</td>\n",
       "      <td>93</td>\n",
       "      <td>12.27</td>\n",
       "      <td>262.4</td>\n",
       "      <td>110</td>\n",
       "      <td>11.81</td>\n",
       "      <td>14.2</td>\n",
       "      <td>4</td>\n",
       "      <td>3.83</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3313</th>\n",
       "      <td>ID</td>\n",
       "      <td>127</td>\n",
       "      <td>408</td>\n",
       "      <td>392-5090</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>102.8</td>\n",
       "      <td>128</td>\n",
       "      <td>17.48</td>\n",
       "      <td>...</td>\n",
       "      <td>95</td>\n",
       "      <td>12.21</td>\n",
       "      <td>191.4</td>\n",
       "      <td>97</td>\n",
       "      <td>8.61</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3314</th>\n",
       "      <td>MO</td>\n",
       "      <td>89</td>\n",
       "      <td>415</td>\n",
       "      <td>373-7713</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>178.7</td>\n",
       "      <td>81</td>\n",
       "      <td>30.38</td>\n",
       "      <td>...</td>\n",
       "      <td>74</td>\n",
       "      <td>19.86</td>\n",
       "      <td>131.9</td>\n",
       "      <td>120</td>\n",
       "      <td>5.94</td>\n",
       "      <td>9.1</td>\n",
       "      <td>4</td>\n",
       "      <td>2.46</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3315</th>\n",
       "      <td>ME</td>\n",
       "      <td>149</td>\n",
       "      <td>415</td>\n",
       "      <td>392-1376</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>18</td>\n",
       "      <td>148.5</td>\n",
       "      <td>106</td>\n",
       "      <td>25.25</td>\n",
       "      <td>...</td>\n",
       "      <td>106</td>\n",
       "      <td>9.73</td>\n",
       "      <td>178.3</td>\n",
       "      <td>98</td>\n",
       "      <td>8.02</td>\n",
       "      <td>6.5</td>\n",
       "      <td>4</td>\n",
       "      <td>1.76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3316</th>\n",
       "      <td>MS</td>\n",
       "      <td>103</td>\n",
       "      <td>510</td>\n",
       "      <td>390-6388</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>29</td>\n",
       "      <td>164.1</td>\n",
       "      <td>111</td>\n",
       "      <td>27.90</td>\n",
       "      <td>...</td>\n",
       "      <td>96</td>\n",
       "      <td>18.62</td>\n",
       "      <td>220.3</td>\n",
       "      <td>108</td>\n",
       "      <td>9.91</td>\n",
       "      <td>12.3</td>\n",
       "      <td>9</td>\n",
       "      <td>3.32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3317</th>\n",
       "      <td>SD</td>\n",
       "      <td>163</td>\n",
       "      <td>415</td>\n",
       "      <td>379-7290</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>197.2</td>\n",
       "      <td>90</td>\n",
       "      <td>33.52</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>16.02</td>\n",
       "      <td>211.1</td>\n",
       "      <td>94</td>\n",
       "      <td>9.50</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8</td>\n",
       "      <td>2.11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3318</th>\n",
       "      <td>OK</td>\n",
       "      <td>52</td>\n",
       "      <td>415</td>\n",
       "      <td>397-9928</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>124.9</td>\n",
       "      <td>131</td>\n",
       "      <td>21.23</td>\n",
       "      <td>...</td>\n",
       "      <td>118</td>\n",
       "      <td>25.54</td>\n",
       "      <td>192.5</td>\n",
       "      <td>106</td>\n",
       "      <td>8.66</td>\n",
       "      <td>11.6</td>\n",
       "      <td>4</td>\n",
       "      <td>3.13</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3319</th>\n",
       "      <td>WY</td>\n",
       "      <td>89</td>\n",
       "      <td>415</td>\n",
       "      <td>378-6924</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>115.4</td>\n",
       "      <td>99</td>\n",
       "      <td>19.62</td>\n",
       "      <td>...</td>\n",
       "      <td>115</td>\n",
       "      <td>17.84</td>\n",
       "      <td>280.9</td>\n",
       "      <td>112</td>\n",
       "      <td>12.64</td>\n",
       "      <td>15.9</td>\n",
       "      <td>6</td>\n",
       "      <td>4.29</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3320</th>\n",
       "      <td>GA</td>\n",
       "      <td>122</td>\n",
       "      <td>510</td>\n",
       "      <td>411-5677</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101</td>\n",
       "      <td>23.80</td>\n",
       "      <td>...</td>\n",
       "      <td>77</td>\n",
       "      <td>16.69</td>\n",
       "      <td>120.1</td>\n",
       "      <td>133</td>\n",
       "      <td>5.40</td>\n",
       "      <td>9.7</td>\n",
       "      <td>4</td>\n",
       "      <td>2.62</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3321</th>\n",
       "      <td>VT</td>\n",
       "      <td>60</td>\n",
       "      <td>415</td>\n",
       "      <td>400-2738</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>193.9</td>\n",
       "      <td>118</td>\n",
       "      <td>32.96</td>\n",
       "      <td>...</td>\n",
       "      <td>110</td>\n",
       "      <td>7.23</td>\n",
       "      <td>210.1</td>\n",
       "      <td>134</td>\n",
       "      <td>9.45</td>\n",
       "      <td>13.2</td>\n",
       "      <td>8</td>\n",
       "      <td>3.56</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3322</th>\n",
       "      <td>MD</td>\n",
       "      <td>62</td>\n",
       "      <td>408</td>\n",
       "      <td>409-1856</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>321.1</td>\n",
       "      <td>105</td>\n",
       "      <td>54.59</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>22.57</td>\n",
       "      <td>180.5</td>\n",
       "      <td>72</td>\n",
       "      <td>8.12</td>\n",
       "      <td>11.5</td>\n",
       "      <td>2</td>\n",
       "      <td>3.11</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3323</th>\n",
       "      <td>IN</td>\n",
       "      <td>117</td>\n",
       "      <td>415</td>\n",
       "      <td>362-5899</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>118.4</td>\n",
       "      <td>126</td>\n",
       "      <td>20.13</td>\n",
       "      <td>...</td>\n",
       "      <td>97</td>\n",
       "      <td>21.19</td>\n",
       "      <td>227.0</td>\n",
       "      <td>56</td>\n",
       "      <td>10.22</td>\n",
       "      <td>13.6</td>\n",
       "      <td>3</td>\n",
       "      <td>3.67</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3324</th>\n",
       "      <td>WV</td>\n",
       "      <td>159</td>\n",
       "      <td>415</td>\n",
       "      <td>377-1164</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>169.8</td>\n",
       "      <td>114</td>\n",
       "      <td>28.87</td>\n",
       "      <td>...</td>\n",
       "      <td>105</td>\n",
       "      <td>16.80</td>\n",
       "      <td>193.7</td>\n",
       "      <td>82</td>\n",
       "      <td>8.72</td>\n",
       "      <td>11.6</td>\n",
       "      <td>4</td>\n",
       "      <td>3.13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3325</th>\n",
       "      <td>OH</td>\n",
       "      <td>78</td>\n",
       "      <td>408</td>\n",
       "      <td>368-8555</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>193.4</td>\n",
       "      <td>99</td>\n",
       "      <td>32.88</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>9.94</td>\n",
       "      <td>243.3</td>\n",
       "      <td>109</td>\n",
       "      <td>10.95</td>\n",
       "      <td>9.3</td>\n",
       "      <td>4</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3326</th>\n",
       "      <td>OH</td>\n",
       "      <td>96</td>\n",
       "      <td>415</td>\n",
       "      <td>347-6812</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>106.6</td>\n",
       "      <td>128</td>\n",
       "      <td>18.12</td>\n",
       "      <td>...</td>\n",
       "      <td>87</td>\n",
       "      <td>24.21</td>\n",
       "      <td>178.9</td>\n",
       "      <td>92</td>\n",
       "      <td>8.05</td>\n",
       "      <td>14.9</td>\n",
       "      <td>7</td>\n",
       "      <td>4.02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3327</th>\n",
       "      <td>SC</td>\n",
       "      <td>79</td>\n",
       "      <td>415</td>\n",
       "      <td>348-3830</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>134.7</td>\n",
       "      <td>98</td>\n",
       "      <td>22.90</td>\n",
       "      <td>...</td>\n",
       "      <td>68</td>\n",
       "      <td>16.12</td>\n",
       "      <td>221.4</td>\n",
       "      <td>128</td>\n",
       "      <td>9.96</td>\n",
       "      <td>11.8</td>\n",
       "      <td>5</td>\n",
       "      <td>3.19</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3328</th>\n",
       "      <td>AZ</td>\n",
       "      <td>192</td>\n",
       "      <td>415</td>\n",
       "      <td>414-4276</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>36</td>\n",
       "      <td>156.2</td>\n",
       "      <td>77</td>\n",
       "      <td>26.55</td>\n",
       "      <td>...</td>\n",
       "      <td>126</td>\n",
       "      <td>18.32</td>\n",
       "      <td>279.1</td>\n",
       "      <td>83</td>\n",
       "      <td>12.56</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "      <td>2.67</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3329</th>\n",
       "      <td>WV</td>\n",
       "      <td>68</td>\n",
       "      <td>415</td>\n",
       "      <td>370-3271</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>231.1</td>\n",
       "      <td>57</td>\n",
       "      <td>39.29</td>\n",
       "      <td>...</td>\n",
       "      <td>55</td>\n",
       "      <td>13.04</td>\n",
       "      <td>191.3</td>\n",
       "      <td>123</td>\n",
       "      <td>8.61</td>\n",
       "      <td>9.6</td>\n",
       "      <td>4</td>\n",
       "      <td>2.59</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3330</th>\n",
       "      <td>RI</td>\n",
       "      <td>28</td>\n",
       "      <td>510</td>\n",
       "      <td>328-8230</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>180.8</td>\n",
       "      <td>109</td>\n",
       "      <td>30.74</td>\n",
       "      <td>...</td>\n",
       "      <td>58</td>\n",
       "      <td>24.55</td>\n",
       "      <td>191.9</td>\n",
       "      <td>91</td>\n",
       "      <td>8.64</td>\n",
       "      <td>14.1</td>\n",
       "      <td>6</td>\n",
       "      <td>3.81</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3331</th>\n",
       "      <td>CT</td>\n",
       "      <td>184</td>\n",
       "      <td>510</td>\n",
       "      <td>364-6381</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>213.8</td>\n",
       "      <td>105</td>\n",
       "      <td>36.35</td>\n",
       "      <td>...</td>\n",
       "      <td>84</td>\n",
       "      <td>13.57</td>\n",
       "      <td>139.2</td>\n",
       "      <td>137</td>\n",
       "      <td>6.26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3332</th>\n",
       "      <td>TN</td>\n",
       "      <td>74</td>\n",
       "      <td>415</td>\n",
       "      <td>400-4344</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>25</td>\n",
       "      <td>234.4</td>\n",
       "      <td>113</td>\n",
       "      <td>39.85</td>\n",
       "      <td>...</td>\n",
       "      <td>82</td>\n",
       "      <td>22.60</td>\n",
       "      <td>241.4</td>\n",
       "      <td>77</td>\n",
       "      <td>10.86</td>\n",
       "      <td>13.7</td>\n",
       "      <td>4</td>\n",
       "      <td>3.70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3333 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     State  Account Length  Area Code     Phone Intl Plan VMail Plan  \\\n",
       "0       KS             128        415  382-4657        no        yes   \n",
       "1       OH             107        415  371-7191        no        yes   \n",
       "2       NJ             137        415  358-1921        no         no   \n",
       "3       OH              84        408  375-9999       yes         no   \n",
       "4       OK              75        415  330-6626       yes         no   \n",
       "5       AL             118        510  391-8027       yes         no   \n",
       "6       MA             121        510  355-9993        no        yes   \n",
       "7       MO             147        415  329-9001       yes         no   \n",
       "8       LA             117        408  335-4719        no         no   \n",
       "9       WV             141        415  330-8173       yes        yes   \n",
       "10      IN              65        415  329-6603        no         no   \n",
       "11      RI              74        415  344-9403        no         no   \n",
       "12      IA             168        408  363-1107        no         no   \n",
       "13      MT              95        510  394-8006        no         no   \n",
       "14      IA              62        415  366-9238        no         no   \n",
       "15      NY             161        415  351-7269        no         no   \n",
       "16      ID              85        408  350-8884        no        yes   \n",
       "17      VT              93        510  386-2923        no         no   \n",
       "18      VA              76        510  356-2992        no        yes   \n",
       "19      TX              73        415  373-2782        no         no   \n",
       "20      FL             147        415  396-5800        no         no   \n",
       "21      CO              77        408  393-7984        no         no   \n",
       "22      AZ             130        415  358-1958        no         no   \n",
       "23      SC             111        415  350-2565        no         no   \n",
       "24      VA             132        510  343-4696        no         no   \n",
       "25      NE             174        415  331-3698        no         no   \n",
       "26      WY              57        408  357-3817        no        yes   \n",
       "27      MT              54        408  418-6412        no         no   \n",
       "28      MO              20        415  353-2630        no         no   \n",
       "29      HI              49        510  410-7789        no         no   \n",
       "...    ...             ...        ...       ...       ...        ...   \n",
       "3303    WI             114        415  373-7308        no        yes   \n",
       "3304    IL              71        510  330-7137       yes         no   \n",
       "3305    IN              58        415  406-8445        no        yes   \n",
       "3306    AL             106        408  404-5283        no        yes   \n",
       "3307    OK             172        408  398-3632        no         no   \n",
       "3308    IA              45        415  399-5763        no         no   \n",
       "3309    VT             100        408  340-9449       yes         no   \n",
       "3310    NY              94        415  363-1123        no         no   \n",
       "3311    LA             128        415  361-2170        no         no   \n",
       "3312    SC             181        408  406-6304        no         no   \n",
       "3313    ID             127        408  392-5090        no         no   \n",
       "3314    MO              89        415  373-7713        no         no   \n",
       "3315    ME             149        415  392-1376        no        yes   \n",
       "3316    MS             103        510  390-6388        no        yes   \n",
       "3317    SD             163        415  379-7290       yes         no   \n",
       "3318    OK              52        415  397-9928        no         no   \n",
       "3319    WY              89        415  378-6924        no         no   \n",
       "3320    GA             122        510  411-5677       yes         no   \n",
       "3321    VT              60        415  400-2738        no         no   \n",
       "3322    MD              62        408  409-1856        no         no   \n",
       "3323    IN             117        415  362-5899        no         no   \n",
       "3324    WV             159        415  377-1164        no         no   \n",
       "3325    OH              78        408  368-8555        no         no   \n",
       "3326    OH              96        415  347-6812        no         no   \n",
       "3327    SC              79        415  348-3830        no         no   \n",
       "3328    AZ             192        415  414-4276        no        yes   \n",
       "3329    WV              68        415  370-3271        no         no   \n",
       "3330    RI              28        510  328-8230        no         no   \n",
       "3331    CT             184        510  364-6381       yes         no   \n",
       "3332    TN              74        415  400-4344        no        yes   \n",
       "\n",
       "      VMail Message  Day Mins  Day Calls  Day Charge  ...  Eve Calls  \\\n",
       "0                25     265.1        110       45.07  ...         99   \n",
       "1                26     161.6        123       27.47  ...        103   \n",
       "2                 0     243.4        114       41.38  ...        110   \n",
       "3                 0     299.4         71       50.90  ...         88   \n",
       "4                 0     166.7        113       28.34  ...        122   \n",
       "5                 0     223.4         98       37.98  ...        101   \n",
       "6                24     218.2         88       37.09  ...        108   \n",
       "7                 0     157.0         79       26.69  ...         94   \n",
       "8                 0     184.5         97       31.37  ...         80   \n",
       "9                37     258.6         84       43.96  ...        111   \n",
       "10                0     129.1        137       21.95  ...         83   \n",
       "11                0     187.7        127       31.91  ...        148   \n",
       "12                0     128.8         96       21.90  ...         71   \n",
       "13                0     156.6         88       26.62  ...         75   \n",
       "14                0     120.7         70       20.52  ...         76   \n",
       "15                0     332.9         67       56.59  ...         97   \n",
       "16               27     196.4        139       33.39  ...         90   \n",
       "17                0     190.7        114       32.42  ...        111   \n",
       "18               33     189.7         66       32.25  ...         65   \n",
       "19                0     224.4         90       38.15  ...         88   \n",
       "20                0     155.1        117       26.37  ...         93   \n",
       "21                0      62.4         89       10.61  ...        121   \n",
       "22                0     183.0        112       31.11  ...         99   \n",
       "23                0     110.4        103       18.77  ...        102   \n",
       "24                0      81.1         86       13.79  ...         72   \n",
       "25                0     124.3         76       21.13  ...        112   \n",
       "26               39     213.0        115       36.21  ...        112   \n",
       "27                0     134.3         73       22.83  ...        100   \n",
       "28                0     190.0        109       32.30  ...         84   \n",
       "29                0     119.3        117       20.28  ...        109   \n",
       "...             ...       ...        ...         ...  ...        ...   \n",
       "3303             26     137.1         88       23.31  ...        125   \n",
       "3304              0     186.1        114       31.64  ...        140   \n",
       "3305             22     224.1        127       38.10  ...         85   \n",
       "3306             29      83.6        131       14.21  ...        131   \n",
       "3307              0     203.9        109       34.66  ...        123   \n",
       "3308              0     211.3         87       35.92  ...         97   \n",
       "3309              0     219.4        112       37.30  ...        102   \n",
       "3310              0     190.4         91       32.37  ...        107   \n",
       "3311              0     147.7         94       25.11  ...         83   \n",
       "3312              0     229.9        130       39.08  ...         93   \n",
       "3313              0     102.8        128       17.48  ...         95   \n",
       "3314              0     178.7         81       30.38  ...         74   \n",
       "3315             18     148.5        106       25.25  ...        106   \n",
       "3316             29     164.1        111       27.90  ...         96   \n",
       "3317              0     197.2         90       33.52  ...        113   \n",
       "3318              0     124.9        131       21.23  ...        118   \n",
       "3319              0     115.4         99       19.62  ...        115   \n",
       "3320              0     140.0        101       23.80  ...         77   \n",
       "3321              0     193.9        118       32.96  ...        110   \n",
       "3322              0     321.1        105       54.59  ...        122   \n",
       "3323              0     118.4        126       20.13  ...         97   \n",
       "3324              0     169.8        114       28.87  ...        105   \n",
       "3325              0     193.4         99       32.88  ...         88   \n",
       "3326              0     106.6        128       18.12  ...         87   \n",
       "3327              0     134.7         98       22.90  ...         68   \n",
       "3328             36     156.2         77       26.55  ...        126   \n",
       "3329              0     231.1         57       39.29  ...         55   \n",
       "3330              0     180.8        109       30.74  ...         58   \n",
       "3331              0     213.8        105       36.35  ...         84   \n",
       "3332             25     234.4        113       39.85  ...         82   \n",
       "\n",
       "      Eve Charge  Night Mins  Night Calls  Night Charge  Intl Mins  \\\n",
       "0          16.78       244.7           91         11.01       10.0   \n",
       "1          16.62       254.4          103         11.45       13.7   \n",
       "2          10.30       162.6          104          7.32       12.2   \n",
       "3           5.26       196.9           89          8.86        6.6   \n",
       "4          12.61       186.9          121          8.41       10.1   \n",
       "5          18.75       203.9          118          9.18        6.3   \n",
       "6          29.62       212.6          118          9.57        7.5   \n",
       "7           8.76       211.8           96          9.53        7.1   \n",
       "8          29.89       215.8           90          9.71        8.7   \n",
       "9          18.87       326.4           97         14.69       11.2   \n",
       "10         19.42       208.8          111          9.40       12.7   \n",
       "11         13.89       196.0           94          8.82        9.1   \n",
       "12          8.92       141.1          128          6.35       11.2   \n",
       "13         21.05       192.3          115          8.65       12.3   \n",
       "14         26.11       203.0           99          9.14       13.1   \n",
       "15         27.01       160.6          128          7.23        5.4   \n",
       "16         23.88        89.3           75          4.02       13.8   \n",
       "17         18.55       129.6          121          5.83        8.1   \n",
       "18         18.09       165.7          108          7.46       10.0   \n",
       "19         13.56       192.8           74          8.68       13.0   \n",
       "20         20.37       208.8          133          9.40       10.6   \n",
       "21         14.44       209.6           64          9.43        5.7   \n",
       "22          6.20       181.8           78          8.18        9.5   \n",
       "23         11.67       189.6          105          8.53        7.7   \n",
       "24         20.84       237.0          115         10.67       10.3   \n",
       "25         23.55       250.7          115         11.28       15.5   \n",
       "26         16.24       182.7          115          8.22        9.5   \n",
       "27         13.22       102.1           68          4.59       14.7   \n",
       "28         21.95       181.5          102          8.17        6.3   \n",
       "29         18.28       178.7           90          8.04       11.1   \n",
       "...          ...         ...          ...           ...        ...   \n",
       "3303       13.23       247.6           94         11.14       11.5   \n",
       "3304       16.88       206.5           80          9.29       13.8   \n",
       "3305       20.30       174.2           86          7.84       11.5   \n",
       "3306       17.33       229.5           73         10.33        8.1   \n",
       "3307       19.89       160.7           65          7.23       17.8   \n",
       "3308       14.08       265.9           72         11.97       13.3   \n",
       "3309       19.18       255.3           95         11.49       12.0   \n",
       "3310        7.82       224.8          108         10.12       13.6   \n",
       "3311       24.08       188.3          124          8.47        6.9   \n",
       "3312       12.27       262.4          110         11.81       14.2   \n",
       "3313       12.21       191.4           97          8.61       10.0   \n",
       "3314       19.86       131.9          120          5.94        9.1   \n",
       "3315        9.73       178.3           98          8.02        6.5   \n",
       "3316       18.62       220.3          108          9.91       12.3   \n",
       "3317       16.02       211.1           94          9.50        7.8   \n",
       "3318       25.54       192.5          106          8.66       11.6   \n",
       "3319       17.84       280.9          112         12.64       15.9   \n",
       "3320       16.69       120.1          133          5.40        9.7   \n",
       "3321        7.23       210.1          134          9.45       13.2   \n",
       "3322       22.57       180.5           72          8.12       11.5   \n",
       "3323       21.19       227.0           56         10.22       13.6   \n",
       "3324       16.80       193.7           82          8.72       11.6   \n",
       "3325        9.94       243.3          109         10.95        9.3   \n",
       "3326       24.21       178.9           92          8.05       14.9   \n",
       "3327       16.12       221.4          128          9.96       11.8   \n",
       "3328       18.32       279.1           83         12.56        9.9   \n",
       "3329       13.04       191.3          123          8.61        9.6   \n",
       "3330       24.55       191.9           91          8.64       14.1   \n",
       "3331       13.57       139.2          137          6.26        5.0   \n",
       "3332       22.60       241.4           77         10.86       13.7   \n",
       "\n",
       "      Intl Calls  Intl Charge  CustServ Calls  Churn  \n",
       "0              3         2.70               1      0  \n",
       "1              3         3.70               1      0  \n",
       "2              5         3.29               0      0  \n",
       "3              7         1.78               2      0  \n",
       "4              3         2.73               3      0  \n",
       "5              6         1.70               0      0  \n",
       "6              7         2.03               3      0  \n",
       "7              6         1.92               0      0  \n",
       "8              4         2.35               1      0  \n",
       "9              5         3.02               0      0  \n",
       "10             6         3.43               4      1  \n",
       "11             5         2.46               0      0  \n",
       "12             2         3.02               1      0  \n",
       "13             5         3.32               3      0  \n",
       "14             6         3.54               4      0  \n",
       "15             9         1.46               4      1  \n",
       "16             4         3.73               1      0  \n",
       "17             3         2.19               3      0  \n",
       "18             5         2.70               1      0  \n",
       "19             2         3.51               1      0  \n",
       "20             4         2.86               0      0  \n",
       "21             6         1.54               5      1  \n",
       "22            19         2.57               0      0  \n",
       "23             6         2.08               2      0  \n",
       "24             2         2.78               0      0  \n",
       "25             5         4.19               3      0  \n",
       "26             3         2.57               0      0  \n",
       "27             4         3.97               3      0  \n",
       "28             6         1.70               0      0  \n",
       "29             1         3.00               1      0  \n",
       "...          ...          ...             ...    ...  \n",
       "3303           7         3.11               2      0  \n",
       "3304           5         3.73               4      1  \n",
       "3305           7         3.11               2      0  \n",
       "3306           3         2.19               1      0  \n",
       "3307           4         4.81               4      0  \n",
       "3308           6         3.59               1      0  \n",
       "3309           4         3.24               4      0  \n",
       "3310          17         3.67               2      0  \n",
       "3311           5         1.86               2      0  \n",
       "3312           4         3.83               2      0  \n",
       "3313           5         2.70               1      0  \n",
       "3314           4         2.46               1      0  \n",
       "3315           4         1.76               0      0  \n",
       "3316           9         3.32               0      0  \n",
       "3317           8         2.11               1      0  \n",
       "3318           4         3.13               2      0  \n",
       "3319           6         4.29               3      0  \n",
       "3320           4         2.62               4      1  \n",
       "3321           8         3.56               3      0  \n",
       "3322           2         3.11               4      1  \n",
       "3323           3         3.67               5      1  \n",
       "3324           4         3.13               1      0  \n",
       "3325           4         2.51               2      0  \n",
       "3326           7         4.02               1      0  \n",
       "3327           5         3.19               2      0  \n",
       "3328           6         2.67               2      0  \n",
       "3329           4         2.59               3      0  \n",
       "3330           6         3.81               2      0  \n",
       "3331          10         1.35               2      0  \n",
       "3332           4         3.70               0      0  \n",
       "\n",
       "[3333 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、探索性数据分析\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aaa38debe0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "churn_df.Churn.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5NBeXXg58McklwA40F5cuAObRXFx66HQULEkTNZRgWlWnAqeObU9yDbAnsKSq1rbNhwMvBNZV1fJ2+xuS7E9zgdRJVbVyGHVK0gy0Gnh2km2BZwLXA/v2xs8kvYtLH0Z7cSlwY5Jdk2xZVfdOV+GStDEjvcF+VV1fVedX1Z19bfdW1bLeoNrXfltVnWcolaT7uRJ4KPBm4GnAcga7uPR+vLuJpC4Z1qF8SdJwvB84pqquTfJQ4DqaC0Z7NnZx6f1U1VJgaW953rx5NYyiJWki/EpSSZpZtgWe3j5/LvBLGOjiUknqLGdMJWlm+Ufg4+1ForcCbwR2YoIXl05X0ZI0EQZTSZpBquoamlnR+5noxaWS1GUGU0maBarqepor9Pvb7gWWTU9FkjQ4zzGVJElSJxhMJUmS1AkeypckddL8JZM7C2Hle180xZVIGhVnTCVJktQJBlNJkiR1gsFUkiRJnWAwlSRJUicYTCVJktQJBlNJkiR1gsFUkiRJnWAwlSRJUicYTCVJktQJBlNJkiR1gsFUkiRJnbDVdBcgSVIXzF+ybFLbrXzvi6a4EmnzNeEZ0ySPGGYhkjTbOY5K0oYNcij/S0OrQpI2D46jkrQBgwTTTyX506FVIkmzn+OoJG3AIOeYHgzsleQtwC+BqqrnDacsSZqVHEclaQMGCaYLh1WEJG0mFk53AZLUZRMOplX1Y4Ak2wN3V9VdQ6tKkmYhx1FJ2rBBrso/PMmPgW8Bf5XkHcMrS5JmH8dRSdqwQS5+egfwDOAnwAeAw4dSkSTNXo6jkrQBgwTTu4Ff0Jys/xtg3XBKkqRZy3FUkjZgkIufzgG+B+yQ5AvAp4dTkiTNWo6jkrQBg1z8dFKSy4EnA9dV1fXDK0uSZh/HUUnasEEuftoGeD7wXOCQJA/dyPpbJ7k4yQHt8qIk1yW5on08rm0/Ksm1SZYneXyvryTLknw1ySlJMtk3KEldMeg4Kkmbm0HOMT0X+F1gBbArcP76VkzyIOAiYPe+5v2BV1bVAe3jpiR7Am8C9gYWASe0674L+HZV7QvcBrx6gDolqasmPI5K0uZokGD6sKp6S1WdU1V/B2y3kfWPpLklSs9+wIlJvpvkhHYW9CDggqpaV1U3Arsm2RI4BDiz3e4imm9LeYAki5Os6j3WrFkzwNuRpJEbdByVpM3KRs8xTdKb9VyV5HU0n/SfRTOTOa6qurtdv7ePrYDFVfXZJFsDlwAH0AzKt/RtuhaYO6b9DmCX9fSzFFjaW543b15t7P1I0qhNZhyVpM3RRC5+OgMoIMDr2kfatgmpqnuSXNw+/3WS7wFPAu4Etu1bdbt23732u/vaJGmm2uRxdKwkC4H9qurIJE8AzgbuAc6oqlPadY6iOU3qv4FF7ZEpSeqsjQbTqjoQIMmLgafz25A44QE1yROBU5I8H9gGeCFwHvAb4J3AB9uv6NsZuJ1mNuEgmlupLABunmhfktQ1UzGO9kvyGOBvgX3bprOAJcDlwBeTXALsQHMO/wJgHs05/IdO8i1I0kgMch/T9wPvowmTMNiM6Q1JrgBuoDlcf1JVXQ2QZIskpwOPBT7Wzq6eCFyYZC/gVcCLB6hTkrpq0uNoT5ItaGZHrwNem+QcYLeqWt6+fhlwIPAw2nP4gRuT7Jpky6q6dwrehyQNxSDB9HJgPvCfE92gqhb2PX8nzezoWIfTzKCu6w2sbZDdn2ZwPamqVg5QpyR11cDj6Dj+lObC1bfSfKD/Nvc/V793Xv7WjH8O/+r+nSVZDCzuLe+www6bUJokbZpBgunzaG5t8rvt8pRcaNR+el82TvttNIf7JWm2mIpx9FnAKVV1C3BLkl8AT+t7fey5+mPb78eLSCV1ySDB9AfASjbtk74kbc6mYhy9HtgDIMlcmnPzVyXZpapupTmn9N9ozs0f7xx+SeqsQYLpL2hukr9/X9tXprYcSZrVpmIcPQ04OcmXgZ1oLnr6NfD5JFcDTwWOrKq1453Dv6lvQJKGaZBgunLMsod7JGkwK8csDzyOVtVdwJ+NbU9yDbAnsKSq1rbNDziHX5K6bDLB9CHAS7j/SfWSpI1b2f455eNoVV1Pc5i/v23cc/glqasmHEyr6oy+xY8m+cgQ6pGkWctxVJI2bMLBtO8r9aC55ciTp74cSZq9HEclacMGOZTf+0o9aL4q9MSpL0eSZjXHUUnagC0GWPdzwK4099/bg+bbSyRJE+c4KkkbMMiM6V8A+1eV98GTpMlxHJWkDRhkxvRHwK+GVYgkbQYcRyVpAwaZMf0JcE2STwNrAKrq2KFUJUmzk+OoJG3AIMF0RfuQJE2O46gkbcBk72MqSRqQ46gkbdgg55hKkiRJQ2MwlSRJUicYTCVJktQJBlNJkiR1gsFUkiRJnWAwlSRJUicYTCVJktQJBlNJkiR1gsFUkiRJnWAwlSRJUicYTCVJktQJBlNJkiR1gsFUkiRJnWAwlSRJUicYTCVJktQJBlNJkiR1gsFUkiRJnWAwlSRJUidsNd0FSJK0OZq/ZNmktlv53hdNcSVSdwxtxjTJ1kkuTnJAu/yEJN9IsiLJor71jkpybZLlSR7ftm2TZFmSryY5JUmGVackSZK6YSjBNMmDgIuA3fuazwKWAPsCr0iye5I9gTcBewOLgBPadd8FfLuq9gVuA149jDolSZLUHcM8x/RI4FsASXYEdquq5VVVwGXAgcBBwAVVta6qbgR2TbIlcAhwZrufi4CDx+sgyeIkq3qPNWvWDPHtSFJ3JDkvycIkc5NcmeSqJMf2vf7S9mjUlUn2ns5aJWmihhJMq+ruqlrV17QdcEvf8h3ALuO0rwXmjmnvrTteP0ural7vMWfOnKl6C5LUWUn+GPiDdvHDwGlVtR8wP8k+SXYCPkTz4f8l/PZolCR12qgufroT2LZveTsgE2i/u69NkjZ7SXYG/g44uW06CFjYPu8dYdoJuLKqft5uc3uSR1fVj0dcriQNZCS3i6qqOwCS9GY+FwA3AytoBlWSbA/sDNze3963riQJPgq8Bfhlu3xXVa1rn6/vaNR6jzx5SpSkLhnl7aKOBz6f5GrgqcCRVbU2yRZJTgceC3ysqu5JciJwYZK9gFcBLx5hnZLUSUleD/ygqq5K8vy2+d6+VTZ2NOoBqmopsLS3PG/evJrSoiVpAEMNplW1sO/5OUmuAfYEllTV2valw4EXAuuqanm77g1J9qe5QOqkqlo5zDolaYZ4KbBjkiuA+cBdwCOTLKiqb/HbI0xXA+9qb7W3BfD7wI+mpWJJGsBIb7BfVdcD149puxd4wF2Gq+o24LwRlSZJnVdV9x09SnIMsJImcJ6Z5FLg5cCzq2p1kq8Bn6UZ56+qqtWjr1iSBuM3P0nSDFRVx/SeJzmM5n7Qx7cf6qmqNyZ5HvAQ4JJpKVKSBmQwlaQZrj3daeU47ctHXowkbYKRXJUvSZIkbYzBVJIkSZ1gMJUkSVInGEwlSZLUCQZTSZIkdYLBVJIkSZ1gMJUkSVInGEwlSZLUCQZTSZIkdYLBVJIkSZ1gMJUkSVInGEwlSZLUCQZTSZIkdYLBVJIkSZ1gMJUkSVInGEwlSZLUCQZTSZIkdYLBVJIkSZ1gMJUkSVInGEwlSZLUCQZTSZIkdYLBVJIkSZ1gMJUkSVInGEwlSZLUCQZTSZIkdYLBVJIkSZ1gMJUkSVInGEwlSZLUCQZTSZIkdcJIg2mSm5Jc0T6OSzI3yZVJrkpybN96L01ybfva3qOsUZIkSdNjq1F1lORxwDVV9Ud9becDp1XVvyY5M8k+wE3Ah4BnAL8BLgWeNao6JanLkswBzgG2AR4GHAXcAZwN3AOcUVWntOseBSwC/htYVFU3TkvRkjRBIwumwHOApyf5Ks1M7V8BBwEL29cvAg4GdgKurKqfAyS5Pcmjq+rHY3eYZDGwuLe8ww47DPUNSFIHHAGcW1XnJjkYOA7YEVgCXA58McklwA7Am4AFwDzgBODQ6SlZkiZmlMH0WuAFVXVTkgOBDwB3VdW69vU7gF2Am4Fb+rbrtT8gmFbVUmBpb3nevHk1pNolqROq6uS+xZ2BnwJPrqrlAEkuAw6kmU29oB1jb0yya5Itq+rekRctSRM0ymB6XVXd1T7/DvAkYF3f69sBAe4Eth2nXZrV5i9ZNt0ldNLK975oukvopCRzgbfTHKrfo++l3of5rbn/h/y1wFxg9Zj9eORJUmeM8uKn05Mc0j5/JfAt4PtJFrRtC2hmS68GnpvGlsDvAz8aYZ2S1GlJHgScDxwNXMP4H+Yn9CG/qpZW1bzeY86cOcMrXJI2YpQzpkcD5yX5ALAKeAMwHzgzyaXAy4FnV9XqJF8DPtvWd1VVrV7PPiVps9J+YD8PWFZVn2rbSLJLVd1K8yH/32g+6L8T+GCS7WkO+98+TWVL0oSMLJi2V4M+Y0zzj5McBuwNHF9Vt7XrvjHJ84CHAJeMqkZJmgFeDxwGPDzJ4TRh83jg80muBp4KHFlVa5NskeR04LHAx6rqnukqWpImYpQzpuOqqpXAynHal4+8GEnquKo6FTh1bHuSa4A9gSVVtbZtPhx4IbDOMVXSTDDtwVSStOmq6nrg+jFt9wJeVSdpxjCYSpK0mZjs3T+8O4ZGZaRfSSpJkiStj8FUkiRJnWAwlSRJUicYTCVJktQJBlNJkiR1gsFUkiRJnWAwlSRJUicYTCVJktQJBlNJkiR1gsFUkiRJnWAwlSRJUicYTCVJktQJBlNJkiR1gsFUkiRJnWAwlSRJUicYTCVJktQJBlNJkiR1gsFUkiRJnWAwlSRJUidsNd0FSJKk2Wn+kmWT2m7le180xZVopnDGVJIkSZ1gMJUkSVInGEwlSZLUCQZTSZIkdYLBVJIkSZ1gMJUkSVInGEwlSZLUCd7HVJIkzQreN3Xmc8ZUkiRJndDpYJrk3Um+k+TiJHOnux5JmmkcRyXNJJ09lJ/kUGAfYAFwAPBu4H9OZ02SNJM4jkrD5akDU6+zwRQ4BPhEVf0myeXAP093QZI0wziOSrPIqIPwZPvblD5TVZPudJiS/AtwflV9oV2+uaoeM2adxcDivqadgdWjq3LGmAOsme4iNKP4OzO+uVX14OkuYqJGPI6O+nfG/mZ+n/Y3s/vblD7XO5Z2ecb0TmDbvuXtx65QVUuBpSOraIZKsqqq5k13HZo5/J2ZNUY2jo76d8b+Zn6f9jez+xtWn12++GkFcBBAkj2A26e3HEmacRxHJc0oXQ6mFwF7J/kw8Engg9NcjyTNNI6jkmaUzh7Kr6q7kuwHHAqcWVXfmO6aZjBPd9Cg/J2ZBUY8jo76d8b+Zn6f9jez+xtKn529+EmSJEmbly4fypckSdJmxGAqSZKkTjCYSpIkqRMMppIkiSQvme4ahiXJ45I8ZMh9/M4w97+5MJjOQkkekeSUJP+e5D+T3J7kuiSvn+7aJGlQSZ6R5NIk/5Lkd/vazx9inyMdR5O8OcllSV6T5AVJrkmyPMmThtTfa8c+gPe0fw6jv2PbPxckuSLJ6iSfT/KoYfTX1+/DklwLfAK4KclrhtjdzUnOSrLbEPsAIMnjk3wpyTeT/HWSrUfQ5x5JzktydJL/keTa9vf0wKnsp7O3i9ImOQP4p6pa1Gtob659fpJrq+pb01eauijJc9b3WlV9ZZS1aGYY8e/MR4C3A7sAFyd5e1V9FthpivvpN7JxNMmTgcNpvhr27cATgVcAjwBOBJ4/VX31ObTt82R++5WSDwHmD6EvgP3aP/+Z5j1+B3gV8FFgcl+qvgFJTqX5+3w18NdVdUWSucAVwNlT3V/rG8BJwNlJfgicXFXXDqmvk4D3AStpbtl0VJI3VtXyIfUHcDpwDPBM4Fhgb+AAmp/hHlPVicF0dtqlqi7vb6iqHya5EtgNMJhqrLcBj6L53UhfewEGU41nlL8zv6iqLwEkuRQ4L8mj276GZZTj6M7AV6rq+0neCRxSVTcCNybZZgr7uU9VvTrJHwP/C/ibqlqR5ICqOnYY/d2/67qifX5qkjcNqZ8V7eP7wIVtx7cnGea3n1VVfR04MMnzgHe1M/zLq+otU9zXVlX1BYAkx9N8w9uSJK+tqoVT3FfPvVV1Wftv4C+rah1wSZJPTmUnBtPZ6dIk/2+qhMcAAAF7SURBVJfm0MUtNN+V/XxgL+Bvp7MwddYf0gze/1BVP53uYjQjjPJ35q4kr6yq89tw8ULgfJoZm2EZ5Tj6NeAfk3y3qpYB/wH3Hf6+bYr7uk9VfTLJV4CTk1wPbDmsvoBHtQFm5ySPqaqbk7wA+PUwOquq09v3dhbwySSn0MxE3zyM/lr3fUBrZy6XJ3kkzez0VPt5e1rC1cAi4PiqOibJK4bQV3+f/0AzO/rQJHsCzwBumMpOvMH+LNUO3IcA2wF30nxyvKiqfjWthamz2nOUqqrume5aNDOM6ncmybbAEVX10b62LYDXV9XHhtjvyMbRdmb0Kf3fzpXkKOC0dmZqqJL8OfDSqjp8iH08FngOcA1wPc2h4aOr6qYh9hngT4FntX2eNqz/B5P8eVX96zD2PU5fOwHHAb8HnF1VHxlBn3OA1wGrgSuB9wMPBY6bylMWDKaSJEnqBK/KlyRJUicYTCVJktQJBlNJkiR1gsFUkiRJnWAwlSRJUif8f+PCRDKreVj/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x320 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#我们先来看一下流失比例， 以及关于打客户电话的个数分布\n",
    "\n",
    "plt.figure(figsize=(10,4),dpi=80)\n",
    "plt.subplot(121)\n",
    "churn_df['Churn'].value_counts().plot(kind='bar') #把用户是否流失累加起来，流失的累加起来，没有流失的累加起来\n",
    "\n",
    "plt.title(u\"stat for churn\") # 设置标题\n",
    "\n",
    "plt.ylabel(u\"number\")  #流失与否的数量，一共3333行，没有流失的约占2700 ，流失的占500左右\n",
    "\n",
    "plt.subplot(122)          \n",
    "churn_df['CustServ Calls'].value_counts().plot(kind='bar') # 客服电话， 客户打电话投诉多那流失率可能会大\n",
    "plt.title(u\"stat for cusServCalls\") \n",
    "plt.ylabel(u\"number\") \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x320 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4),dpi=80)\n",
    "plt.subplot(131)\n",
    "churn_df['Day Mins'].plot(kind='kde') # 白天通话分钟数，图用的kde的图例\n",
    "plt.xlabel(u\"Mins\")# 横轴是分钟数\n",
    "plt.ylabel(u\"density\")  # density：密度\n",
    "plt.title(u\"dis for day mins\") #标题\n",
    "\n",
    "plt.subplot(132)           \n",
    "churn_df['Day Calls'].plot(kind='kde')# 白天打电话个数\n",
    "plt.xlabel(u\"call\")# 客户打电话个数\n",
    "plt.ylabel(u\"density\") #密度\n",
    "plt.title(u\"dis for day calls\") #标题\n",
    "\n",
    "plt.subplot(133)       \n",
    "churn_df['Day Charge'].plot(kind='kde') # 白天收费情况\n",
    "plt.xlabel(u\"Charge\")# 横轴是白天收费情况\n",
    "plt.ylabel(u\"density\") #密度\n",
    "plt.title(u\"dis for day charge\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**特征和分类的关联**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x320 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,4),dpi=80)\n",
    "\n",
    "#查看流失与国际漫游之间的关系\n",
    "int_yes = churn_df['Churn'][churn_df['Intl Plan'] == 'yes'].value_counts() # yes:参与了国际漫游的统计出来\n",
    "int_no = churn_df['Churn'][churn_df['Intl Plan'] == 'no'].value_counts() #no:没有参与国际漫游的统计出来\n",
    "\n",
    "#用DataFrame做图例上的标签 ，在右上角\n",
    "df_int=pd.DataFrame({u'int plan':int_yes, u'no int plan':int_no})\n",
    "\n",
    "df_int.plot(kind='bar', stacked=True)\n",
    "plt.title(u\"statistic between int plan and churn\")\n",
    "plt.xlabel(u\"int or not\") \n",
    "plt.ylabel(u\"number\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x320 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看客户服务电话和结果的关联\n",
    "plt.figure(figsize=(10,4),dpi=80)\n",
    "\n",
    "cus_0 = churn_df['CustServ Calls'][churn_df['Churn'] == 0].value_counts()\n",
    "cus_1 = churn_df['CustServ Calls'][churn_df['Churn'] == 1].value_counts()\n",
    "df=pd.DataFrame({u'churn':cus_1, u'retain':cus_0})\n",
    "df.plot(kind='bar', stacked=True)\n",
    "plt.title(u\"Static between customer service call and churn\")\n",
    "plt.xlabel(u\"Call service\") \n",
    "plt.ylabel(u\"Num\") \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出打三个以上客服服务电话的人流失比例在升高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**特征处理**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     2411\n",
       "yes     922\n",
       "Name: VMail Plan, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df['VMail Plan'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after convert \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Account Length</th>\n",
       "      <th>VMail Message</th>\n",
       "      <th>Day Mins</th>\n",
       "      <th>Day Calls</th>\n",
       "      <th>Day Charge</th>\n",
       "      <th>Eve Mins</th>\n",
       "      <th>Eve Calls</th>\n",
       "      <th>Eve Charge</th>\n",
       "      <th>Night Mins</th>\n",
       "      <th>Night Calls</th>\n",
       "      <th>Night Charge</th>\n",
       "      <th>Intl Mins</th>\n",
       "      <th>Intl Calls</th>\n",
       "      <th>Intl Charge</th>\n",
       "      <th>CustServ Calls</th>\n",
       "      <th>_intlPlan_no</th>\n",
       "      <th>_intlPlan_yes</th>\n",
       "      <th>VMail_no</th>\n",
       "      <th>VMail_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>128</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>197.4</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>107</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>195.5</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>121.2</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>61.9</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>148.3</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Account Length  VMail Message  Day Mins  Day Calls  Day Charge  Eve Mins  \\\n",
       "0             128             25     265.1        110       45.07     197.4   \n",
       "1             107             26     161.6        123       27.47     195.5   \n",
       "2             137              0     243.4        114       41.38     121.2   \n",
       "3              84              0     299.4         71       50.90      61.9   \n",
       "4              75              0     166.7        113       28.34     148.3   \n",
       "\n",
       "   Eve Calls  Eve Charge  Night Mins  Night Calls  Night Charge  Intl Mins  \\\n",
       "0         99       16.78       244.7           91         11.01       10.0   \n",
       "1        103       16.62       254.4          103         11.45       13.7   \n",
       "2        110       10.30       162.6          104          7.32       12.2   \n",
       "3         88        5.26       196.9           89          8.86        6.6   \n",
       "4        122       12.61       186.9          121          8.41       10.1   \n",
       "\n",
       "   Intl Calls  Intl Charge  CustServ Calls  _intlPlan_no  _intlPlan_yes  \\\n",
       "0           3         2.70               1             1              0   \n",
       "1           3         3.70               1             1              0   \n",
       "2           5         3.29               0             1              0   \n",
       "3           7         1.78               2             0              1   \n",
       "4           3         2.73               3             0              1   \n",
       "\n",
       "   VMail_no  VMail_yes  \n",
       "0         0          1  \n",
       "1         0          1  \n",
       "2         1          0  \n",
       "3         1          0  \n",
       "4         1          0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#国际策划活动 yes no\n",
    "dummies_int = pd.get_dummies(churn_df['Intl Plan'], prefix='_intlPlan') #prefix：前缀\n",
    "# VMail Plan：某个策划活动 yes no prefix：前缀\n",
    "dummies_voice = pd.get_dummies(churn_df['VMail Plan'], prefix='VMail')\n",
    "\n",
    "#concat：用来合并2个或者2个以上的数组\n",
    "ds_tmp=pd.concat([churn_df, dummies_int, dummies_voice], axis=1)\n",
    "\n",
    "# 删除州名、地区编号、手机号、用户是否流失、各种策略活动\n",
    "to_drop = ['State','Area Code','Phone','Churn', 'Intl Plan', 'VMail Plan']\n",
    "df = ds_tmp.drop(to_drop,axis=1)\n",
    "\n",
    "print(\"after convert \")\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、建立模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.loc[:,:].values\n",
    "from sklearn.preprocessing import StandardScaler # 标准化\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "y = churn_df['Churn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#训练集测试集划分\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBClassifier在训练集的精确度:.0.98\n",
      "XGBClassifier在测试集的精确度:.0.96\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "xgb = XGBClassifier(learning_rate=0.1, n_estimators=550, max_depth=4, min_child_weight=5, seed=27,\n",
    "                             subsample=0.7, colsample_bytree=0.7, gamma=0.3, reg_alpha=5, reg_lambda=1)\n",
    "xgb.fit(X_train, y_train)\n",
    "print(\"XGBClassifier在训练集的精确度:.{:.2f}\".format(xgb.score(X_train,y_train)))\n",
    "print(\"XGBClassifier在测试集的精确度:.{:.2f}\".format(xgb.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 四、模型评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.99      0.97       566\n",
      "           1       0.96      0.74      0.84       101\n",
      "\n",
      "    accuracy                           0.96       667\n",
      "   macro avg       0.96      0.87      0.91       667\n",
      "weighted avg       0.96      0.96      0.95       667\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = xgb.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "精确度还是比较好的，100个预测会流失的客户中，有96个将会流失，实际上会有135个会流失(recall 0.74)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 19 artists>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = churn_df.columns[0:-1]\n",
    "name=[]\n",
    "importance = []\n",
    "for n,i in sorted(zip(xgb.feature_importances_,x)):\n",
    "    name.append(i)\n",
    "    importance.append(n)\n",
    "plt.figure(figsize=(10,8),dpi=80)\n",
    "plt.barh(name,importance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import auc\n",
    "def plot_roc(y_test,x_test):\n",
    "        y_pred = xgb.predict_proba(X_test)\n",
    "        fpr,tpr,threhols= roc_curve(y_test,y_pred[:,1]) #ROC\n",
    "        auc_s = auc(fpr,tpr)\n",
    "\n",
    "        plt.figure(figsize=(10,6),facecolor='w')\n",
    "        plt.plot(fpr,tpr,c='r',lw=2,label=u'xgb,AUC=%.3f' %auc_s)\n",
    "        plt.plot((0,1),(0,1),c='#a0a0a0',lw=2,ls='--')\n",
    "        plt.xlim(-0.001,1.001)\n",
    "        plt.ylim(-0.001,1.001)\n",
    "        plt.xticks(np.arange(0,1.1,0.1))\n",
    "        plt.yticks(np.arange(0,1.1,0.1))\n",
    "        plt.legend(loc='lower right',fancybox=True,framealpha=0.8,fontsize=12)\n",
    "        plt.xlabel('FPR',fontsize=16)\n",
    "        plt.ylabel('TPR',fontsize=16)\n",
    "        plt.title(\"ROC\")\n",
    "    \n",
    "plot_roc(y_test,X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这样之前我们对用户进行RFM划分，针对高价值用户，有哪些会流失，我们可以提前采取运营手段来防止用户流失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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